Transformer-Based Model Forecasts Airport Terminal Passenger Queues Up to Two Hours Ahead
Researchers have developed a Transformer-based machine learning framework capable of predicting passenger queue lengths and waiting times at airport departure gates and security checkpoints up to two hours in advance. The model learns from historical operational data, capturing temporal patterns and correlations across multiple airport facilities including check-in islands. The system could enable airport operators to proactively manage congestion and reallocate staff before bottlenecks develop.
A research team has proposed a passenger queue forecasting framework designed to improve operational efficiency in airport terminals, with findings accepted for presentation at DASC 2026. The model uses a Transformer-based architecture — a class of deep learning model well-suited to sequential data — to identify temporal dependencies and relationships between different airport facilities such as departure gates, security checkpoints, and check-in islands. Two facility-specific multilayer perceptron (MLP) output heads generate separate predictions for queue length and waiting time at each location type. The system is trained on historical passenger flow data, allowing it to account for time-varying demand and the heterogeneous ways different facilities are used throughout the day. Experimental results indicate accurate forecasts can be produced up to a two-hour horizon, which the authors argue is sufficient for real-time decision support and proactive staff reallocation.
What's missing
The paper does not specify which airport(s) or dataset(s) were used for training and evaluation, making it unclear how well the model generalizes to airports with different layouts, passenger volumes, or operational procedures. Key limitations such as performance under rare disruption events (e.g., flight cancellations, security incidents) and the model's sensitivity to data quality or availability are not addressed in the abstract.
What different sources said
- arXiv cs.LGCenter
Airport Terminal Passenger Queue Forecasting for Departure Gates and Security Checkpoints
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